Coupling remote sensing data with <i>in-situ</i> optical measurements to estimate suspended particulate matter under the Evros river influence (North-East Aegean sea, Greece)

Tsapanou, Athina; Oikonomou, Emmanouil; Drakopoulos, Panagiotis; Πούλος, Σ.; Sylaïos, Georgios

doi:10.1080/01431161.2019.1685713

Cited by 5 publications

(3 citation statements)

References 44 publications

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“…SSC has optically active properties that can be easily correlated against in situ measurements using regression analysis techniques. SSC has been successfully estimated in lacustrine, fluvial, and estuarine environments by several modern spaceborne sensors such as IKONOS, Landsat 8, Sentinel-2, RapidEye, and MODIS [34,[40][41][42][43][44].…”

Section: Suspended Sediment Concentration (Ssc)mentioning

confidence: 99%

Remote Sensing of Turbidity in the Tennessee River Using Landsat 8 Satellite

Hossain

Mathias

Blanton³

2021

Remote Sensing

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The Tennessee River in the United States is one of the most ecologically distinct rivers in the world and serves as a great resource for local residents. However, it is also one of the most polluted rivers in the world, and a leading cause of this pollution is storm water runoff. Satellite remote sensing technology, which has been used successfully to study surface water quality parameters for many years, could be very useful to study and monitor the quality of water in the Tennessee River. This study developed a numerical turbidity estimation model for the Tennessee River and its tributaries in Southeast Tennessee using Landsat 8 satellite imagery coupled with near real-time in situ measurements. The obtained results suggest that a nonlinear regression-based numerical model can be developed using Band 4 (red) surface reflectance values of the Landsat 8 OLI sensor to estimate turbidity in these water bodies with the potential of high accuracy. The accuracy assessment of the estimated turbidity achieved a coefficient of determination (R2) value and root mean square error (RMSE) as high as 0.97 and 1.41 NTU, respectively. The model was also tested on imagery acquired on a different date to assess its potential for routine remote estimation of turbidity and produced encouraging results with R2 value of 0.94 and relatively high RMSE.

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Section: Suspended Sediment Concentration (Ssc)mentioning

confidence: 99%

Remote Sensing of Turbidity in the Tennessee River Using Landsat 8 Satellite

Hossain

Mathias

Blanton³

2021

Remote Sensing

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“…In the downstream part, after dam controlling, water discharge was calculated at about 340 m 3 /s, whilst sediment load was at about 0.5 Mt/year, most of which (~90%) is dissolved sediment [41]. Therefore, the Evros river's discharge forms a plume whose dynamics and distribution is related to coastal hydrodynamic activity [44]. However, the Evros river is characterized by high inter-annual flow fluctuations [45,46] with increasing flood frequency during the last years (water discharge > 2500 m 3 /s), implying an association with both climate variations and dam operation in the river basin [47], which often cause extended economic damages in the riparian and deltaic part [48].…”

Section: Study Areamentioning

confidence: 99%

An Insight into the Factors Controlling Delta Flood Events: The Case of the Evros River Deltaic Plain (NE Aegean Sea)

Πούλος

Karditsa

Hatzaki

et al. 2022

Water

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The present contribution aims to give an insight into the main terrestrial and marine processes leading to delta flooding in the case of the transboundary Evros delta, located at the microtidal NE Aegean Sea, on the basis of recorded flood events in the Evros deltaic plain. The prevailing weather conditions at the onset of the event, along with sea-level rise above the mean state, portray the mechanism for the development of compound flood events and subsequent riparian flooding. This system blocks the riverine water’s seaward exit, resulting in the flooding of the lower deltaic plain. The river discharge is recognized as a secondary factor acting mainly toward the persistence of the events. Several limitations restrict the quantification potential of the relative contribution of the key factors to the development, onset, and duration of a flood. Mitigation of the impacts of such flood events requires intercountry cooperation and a management plan based on a network of environmental monitoring.

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“…Traditional water quality inversion models include empirical models, semi-empirical models, and physical models. Some water quality parameters can be directly inverted by remote sensing after including optically active substances in water bodies, such as chlorophyll a (Chl-a), colored dissolved organic matter (CDOM), and suspended solids (SS) [13,14]. It is worth mentioning that Chl-a content inversion research is used a lot in applications [15].…”

Section: Introductionmentioning

confidence: 99%

Water Quality Chl-a Inversion Based on Spatio-Temporal Fusion and Convolutional Neural Network

Yang

Zhao

et al. 2022

Remote Sensing

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The combination of remote sensing technology and traditional field sampling provides a convenient way to monitor inland water. However, limited by the resolution of remote sensing images and cloud contamination, the current water quality inversion products do not provide both high temporal resolution and high spatial resolution. By using the spatio-temporal fusion (STF) method, high spatial resolution and temporal fusion images were generated with Landsat, Sentinel-2, and GaoFen-2 data. Then, a Chl-a inversion model was designed based on a convolutional neural network (CNN) with the structure of 4-(136-236-340)-1-1. Finally, the results of the Chl-a concentrations were corrected using a pixel correction algorithm. The images generated from STF can maintain the spectral characteristics of the low-resolution images with the R2 between 0.7 and 0.9. The Chl-a inversion results based on the spatio-temporal fused images and CNN were verified with measured data (R2 = 0.803), and then the results were improved (R2 = 0.879) after further combining them with the pixel correction algorithm. The correlation R2 between the Chl-a results of GF2-like and Sentinel-2 were both greater than 0.8. The differences in the spatial distribution of Chl-a concentrations in the BYD lake gradually increased from July to August. Remote sensing water quality inversion based on STF and CNN can effectively achieve high frequency in time and fine resolution in space, which provide a stronger scientific basis for rapid diagnosis of eutrophication in inland lakes.

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Coupling remote sensing data with in-situ optical measurements to estimate suspended particulate matter under the Evros river influence (North-East Aegean sea, Greece)

Cited by 5 publications

References 44 publications

Remote Sensing of Turbidity in the Tennessee River Using Landsat 8 Satellite

Remote Sensing of Turbidity in the Tennessee River Using Landsat 8 Satellite

An Insight into the Factors Controlling Delta Flood Events: The Case of the Evros River Deltaic Plain (NE Aegean Sea)

Water Quality Chl-a Inversion Based on Spatio-Temporal Fusion and Convolutional Neural Network

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